S3 methods for VA conversion
convertVA(x, ...)# S3 method for quali
convertVA(x, to, type, ...)
# S3 method for snellendec
convertVA(x, to, type, ...)
# S3 method for snellen
convertVA(x, to, type, smallstep, noplus, ...)
# S3 method for logmar
convertVA(x, to, type, ...)
# S3 method for etdrs
convertVA(x, to, type, ...)
# S3 method for default
convertVA(x, to, ...)
vector of visual acuities
further arguments passed to methods
to which VA class to convert
which snellen notation. One of "ft", "m" or "dec"
how plus/minus entries are evaluated. Default to increase/decrease snellen fractions by lines. If TRUE, each snellen optotype will be considered equivalent to 0.02 logmar or 1 ETDRS letter (assuming 5 letters in a row in a chart)
ignoring plus/minus entries and just returning the snellen fraction. This overrides the smallstep argument.
vector with visual acuity of class va. See also "VA classes"
For other conversion and theory behind conversion rules see va section VA conversion.
The following rules for plus minus notations will be applied:
if entry -2 to +2 : take same Snellen value
if < -2 : take Snellen value one line below
if > +2: Snellen value one line above
Snellen are unfortunately often entered with "+/-", which is a violation of a psychophysical method designed to assign one unambiguous value to visual acuity, with non-arbitrary thresholds based on psychometric functions. Therefore, transforming "+/-" notation to actual results is in itself problematic and the below suggestion to convert it will remain an approximation to the most likely "true" result. Even more so, as the given conditions should work for charts with 4 or 5 optotypes in a line, and visual acuity is not always tested on such charts. Yet, I believe that the approach is still better than just omitting the letters or (worse) assigning a missing value to those entries.
VA can be snellen feet/meter/decimal, logMAR, ETDRS, or "qualitative" (Counting fingers, etc.)
Snellen fractions need to be either form 6/x or 20/x
ETDRS must be between 0 and 100
logMAR must be between -0.3 and 3.0
Qualitative must be PL, LP, NLP, NPL, HM, CF (any case allowed)
Any element which is not recognized will be converted to NA
Other VA converter:
VAwrapper,
plausibility_methods,
snellen_steps,
va_mixed(),
va(),
which_va()